diff_diff.WildBootstrapResults#

class diff_diff.WildBootstrapResults[source]#

Bases: object

Results from wild cluster bootstrap inference.

se#

Bootstrap standard error of the coefficient.

Type:: float

p_value#

Bootstrap p-value (two-sided).

Type:: float

t_stat_original#

Original t-statistic from the data.

Type:: float

ci_lower#

Lower bound of the confidence interval.

Type:: float

ci_upper#

Upper bound of the confidence interval.

Type:: float

n_clusters#

Number of clusters in the data.

Type:: int

n_bootstrap#

Number of bootstrap replications.

Type:: int

weight_type#

Type of bootstrap weights used (“rademacher”, “webb”, or “mammen”).

Type:: str

alpha#

Significance level used for confidence interval.

Type:: float

bootstrap_distribution#

Full bootstrap distribution of coefficients (if requested).

Type:: np.ndarray, optional

References

Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics, 90(3), 414-427.

Methods

`__init__`(se, p_value, t_stat_original, ...)
`print_summary`()	Print formatted summary to stdout.
`summary`()	Generate formatted summary of bootstrap results.

Attributes

`alpha`
`bootstrap_distribution`
`se`
`p_value`
`t_stat_original`
`ci_lower`
`ci_upper`
`n_clusters`
`n_bootstrap`
`weight_type`

__init__(se, p_value, t_stat_original, ci_lower, ci_upper, n_clusters, n_bootstrap, weight_type, alpha=0.05, bootstrap_distribution=None)#

Parameters:

se (float)
p_value (float)
t_stat_original (float)
ci_lower (float)
ci_upper (float)
n_clusters (int)
n_bootstrap (int)
weight_type (str)
alpha (float)
bootstrap_distribution (ndarray | None)

Return type:

None

classmethod __new__(*args, **kwargs)#